from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-02 14:07:09.387771
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 02, Dec, 2020
Time: 14:07:12
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.0315
Nobs: 127.000 HQIC: -44.2281
Log likelihood: 1328.64 FPE: 2.73904e-20
AIC: -45.0470 Det(Omega_mle): 1.38467e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.594332 0.190414 3.121 0.002
L1.Burgenland 0.136477 0.088452 1.543 0.123
L1.Kärnten -0.309268 0.074348 -4.160 0.000
L1.Niederösterreich 0.058452 0.212806 0.275 0.784
L1.Oberösterreich 0.274067 0.176476 1.553 0.120
L1.Salzburg 0.145902 0.089217 1.635 0.102
L1.Steiermark 0.078519 0.125910 0.624 0.533
L1.Tirol 0.167978 0.083537 2.011 0.044
L1.Vorarlberg 0.024590 0.081174 0.303 0.762
L1.Wien -0.142328 0.168151 -0.846 0.397
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.634879 0.244408 2.598 0.009
L1.Burgenland 0.002636 0.113533 0.023 0.981
L1.Kärnten 0.335456 0.095430 3.515 0.000
L1.Niederösterreich 0.098186 0.273150 0.359 0.719
L1.Oberösterreich -0.238514 0.226518 -1.053 0.292
L1.Salzburg 0.177459 0.114515 1.550 0.121
L1.Steiermark 0.237179 0.161614 1.468 0.142
L1.Tirol 0.137457 0.107225 1.282 0.200
L1.Vorarlberg 0.206691 0.104193 1.984 0.047
L1.Wien -0.565892 0.215832 -2.622 0.009
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.341196 0.082573 4.132 0.000
L1.Burgenland 0.101451 0.038357 2.645 0.008
L1.Kärnten -0.028588 0.032241 -0.887 0.375
L1.Niederösterreich 0.116994 0.092283 1.268 0.205
L1.Oberösterreich 0.274814 0.076529 3.591 0.000
L1.Salzburg -0.014894 0.038689 -0.385 0.700
L1.Steiermark -0.050812 0.054601 -0.931 0.352
L1.Tirol 0.098717 0.036226 2.725 0.006
L1.Vorarlberg 0.142642 0.035201 4.052 0.000
L1.Wien 0.027824 0.072919 0.382 0.703
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.200325 0.097589 2.053 0.040
L1.Burgenland 0.005242 0.045333 0.116 0.908
L1.Kärnten 0.031934 0.038104 0.838 0.402
L1.Niederösterreich 0.064637 0.109066 0.593 0.553
L1.Oberösterreich 0.358933 0.090446 3.968 0.000
L1.Salzburg 0.083225 0.045725 1.820 0.069
L1.Steiermark 0.201299 0.064531 3.119 0.002
L1.Tirol 0.032800 0.042814 0.766 0.444
L1.Vorarlberg 0.114273 0.041603 2.747 0.006
L1.Wien -0.093719 0.086179 -1.087 0.277
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.755380 0.207579 3.639 0.000
L1.Burgenland 0.062879 0.096425 0.652 0.514
L1.Kärnten -0.014055 0.081050 -0.173 0.862
L1.Niederösterreich -0.094724 0.231990 -0.408 0.683
L1.Oberösterreich 0.069727 0.192385 0.362 0.717
L1.Salzburg 0.031255 0.097259 0.321 0.748
L1.Steiermark 0.120494 0.137261 0.878 0.380
L1.Tirol 0.225373 0.091068 2.475 0.013
L1.Vorarlberg 0.037213 0.088492 0.421 0.674
L1.Wien -0.167136 0.183309 -0.912 0.362
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.226674 0.143072 1.584 0.113
L1.Burgenland -0.051497 0.066460 -0.775 0.438
L1.Kärnten -0.016783 0.055863 -0.300 0.764
L1.Niederösterreich 0.173413 0.159897 1.085 0.278
L1.Oberösterreich 0.396473 0.132600 2.990 0.003
L1.Salzburg -0.041138 0.067035 -0.614 0.539
L1.Steiermark -0.055411 0.094606 -0.586 0.558
L1.Tirol 0.203032 0.062768 3.235 0.001
L1.Vorarlberg 0.049517 0.060992 0.812 0.417
L1.Wien 0.128519 0.126344 1.017 0.309
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.287815 0.181097 1.589 0.112
L1.Burgenland 0.073923 0.084124 0.879 0.380
L1.Kärnten -0.083221 0.070710 -1.177 0.239
L1.Niederösterreich -0.130669 0.202394 -0.646 0.519
L1.Oberösterreich -0.105257 0.167841 -0.627 0.531
L1.Salzburg -0.000031 0.084851 -0.000 1.000
L1.Steiermark 0.374473 0.119750 3.127 0.002
L1.Tirol 0.539038 0.079450 6.785 0.000
L1.Vorarlberg 0.235022 0.077203 3.044 0.002
L1.Wien -0.182521 0.159923 -1.141 0.254
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.072663 0.209138 0.347 0.728
L1.Burgenland 0.031734 0.097150 0.327 0.744
L1.Kärnten -0.061914 0.081659 -0.758 0.448
L1.Niederösterreich 0.267686 0.233732 1.145 0.252
L1.Oberösterreich 0.023785 0.193830 0.123 0.902
L1.Salzburg 0.230151 0.097990 2.349 0.019
L1.Steiermark 0.162728 0.138292 1.177 0.239
L1.Tirol 0.049987 0.091752 0.545 0.586
L1.Vorarlberg 0.018786 0.089157 0.211 0.833
L1.Wien 0.213023 0.184686 1.153 0.249
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.637478 0.115995 5.496 0.000
L1.Burgenland -0.010228 0.053882 -0.190 0.849
L1.Kärnten -0.004146 0.045291 -0.092 0.927
L1.Niederösterreich -0.067945 0.129636 -0.524 0.600
L1.Oberösterreich 0.274253 0.107504 2.551 0.011
L1.Salzburg -0.000909 0.054348 -0.017 0.987
L1.Steiermark 0.013271 0.076701 0.173 0.863
L1.Tirol 0.076477 0.050888 1.503 0.133
L1.Vorarlberg 0.190669 0.049449 3.856 0.000
L1.Wien -0.098922 0.102433 -0.966 0.334
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.090207 -0.055518 0.176209 0.226430 0.007397 0.060245 -0.127899 0.104287
Kärnten 0.090207 1.000000 -0.057578 0.180359 0.083534 -0.168672 0.190456 0.018066 0.266172
Niederösterreich -0.055518 -0.057578 1.000000 0.248379 0.079354 0.163961 0.065409 0.053129 0.360294
Oberösterreich 0.176209 0.180359 0.248379 1.000000 0.251646 0.267986 0.069233 0.060835 0.046956
Salzburg 0.226430 0.083534 0.079354 0.251646 1.000000 0.132486 0.042168 0.090849 -0.065595
Steiermark 0.007397 -0.168672 0.163961 0.267986 0.132486 1.000000 0.088919 0.084034 -0.191520
Tirol 0.060245 0.190456 0.065409 0.069233 0.042168 0.088919 1.000000 0.136473 0.087064
Vorarlberg -0.127899 0.018066 0.053129 0.060835 0.090849 0.084034 0.136473 1.000000 0.079377
Wien 0.104287 0.266172 0.360294 0.046956 -0.065595 -0.191520 0.087064 0.079377 1.000000